{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_train = np.array([368,340,376,954,331,556])\n",
    "X_train = np.array([1.7,1.5,1.3,5,1.3,2.2])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(X_train, y_train, label='sample')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=None, normalize=False)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.linear_model import LinearRegression\n",
    "X_train = X_train.reshape(-1,1)\n",
    "lr = LinearRegression()\n",
    "lr.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([593.77076521])"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lr.predict(np.array([[2.8]]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Huber损失"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([589.3155147])"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.linear_model import HuberRegressor\n",
    "huber = HuberRegressor()\n",
    "huber.fit(X_train, y_train)\n",
    "huber.predict(np.array([[2.8]]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "#波士顿房价"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>CRIM</th>\n",
       "      <th>ZN</th>\n",
       "      <th>INDUS</th>\n",
       "      <th>CHAS</th>\n",
       "      <th>NOX</th>\n",
       "      <th>RM</th>\n",
       "      <th>AGE</th>\n",
       "      <th>DIS</th>\n",
       "      <th>RAD</th>\n",
       "      <th>TAX</th>\n",
       "      <th>PTRATIO</th>\n",
       "      <th>B</th>\n",
       "      <th>LSTAT</th>\n",
       "      <th>MEDV</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.00632</td>\n",
       "      <td>18</td>\n",
       "      <td>2.31</td>\n",
       "      <td>0</td>\n",
       "      <td>0.538</td>\n",
       "      <td>6.575</td>\n",
       "      <td>65.2</td>\n",
       "      <td>4.0900</td>\n",
       "      <td>1</td>\n",
       "      <td>296</td>\n",
       "      <td>15</td>\n",
       "      <td>396.90</td>\n",
       "      <td>4.98</td>\n",
       "      <td>24.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.02731</td>\n",
       "      <td>0</td>\n",
       "      <td>7.07</td>\n",
       "      <td>0</td>\n",
       "      <td>0.469</td>\n",
       "      <td>6.421</td>\n",
       "      <td>78.9</td>\n",
       "      <td>4.9671</td>\n",
       "      <td>2</td>\n",
       "      <td>242</td>\n",
       "      <td>17</td>\n",
       "      <td>396.90</td>\n",
       "      <td>9.14</td>\n",
       "      <td>21.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.02729</td>\n",
       "      <td>0</td>\n",
       "      <td>7.07</td>\n",
       "      <td>0</td>\n",
       "      <td>0.469</td>\n",
       "      <td>7.185</td>\n",
       "      <td>61.1</td>\n",
       "      <td>4.9671</td>\n",
       "      <td>2</td>\n",
       "      <td>242</td>\n",
       "      <td>17</td>\n",
       "      <td>392.83</td>\n",
       "      <td>4.03</td>\n",
       "      <td>34.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.03237</td>\n",
       "      <td>0</td>\n",
       "      <td>2.18</td>\n",
       "      <td>0</td>\n",
       "      <td>0.458</td>\n",
       "      <td>6.998</td>\n",
       "      <td>45.8</td>\n",
       "      <td>6.0622</td>\n",
       "      <td>3</td>\n",
       "      <td>222</td>\n",
       "      <td>18</td>\n",
       "      <td>394.63</td>\n",
       "      <td>2.94</td>\n",
       "      <td>33.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.06905</td>\n",
       "      <td>0</td>\n",
       "      <td>2.18</td>\n",
       "      <td>0</td>\n",
       "      <td>0.458</td>\n",
       "      <td>7.147</td>\n",
       "      <td>54.2</td>\n",
       "      <td>6.0622</td>\n",
       "      <td>3</td>\n",
       "      <td>222</td>\n",
       "      <td>18</td>\n",
       "      <td>396.90</td>\n",
       "      <td>5.33</td>\n",
       "      <td>36.2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      CRIM  ZN  INDUS  CHAS    NOX     RM   AGE     DIS  RAD  TAX  PTRATIO  \\\n",
       "0  0.00632  18   2.31     0  0.538  6.575  65.2  4.0900    1  296       15   \n",
       "1  0.02731   0   7.07     0  0.469  6.421  78.9  4.9671    2  242       17   \n",
       "2  0.02729   0   7.07     0  0.469  7.185  61.1  4.9671    2  242       17   \n",
       "3  0.03237   0   2.18     0  0.458  6.998  45.8  6.0622    3  222       18   \n",
       "4  0.06905   0   2.18     0  0.458  7.147  54.2  6.0622    3  222       18   \n",
       "\n",
       "        B  LSTAT  MEDV  \n",
       "0  396.90   4.98  24.0  \n",
       "1  396.90   9.14  21.6  \n",
       "2  392.83   4.03  34.7  \n",
       "3  394.63   2.94  33.4  \n",
       "4  396.90   5.33  36.2  "
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import sys\n",
    "import pandas as pd\n",
    "\n",
    "import seaborn as sns\n",
    "\n",
    "%matplotlib inline\n",
    "\n",
    "df = pd.read_csv(\"D:\\\\Work\\\\OpenCV\\\\lesson\\\\机器学习\\\\1-2机器学习\\\\ppt2\\\\BostonHousePrice_CodeData\\\\boston_housing.csv\")\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 506 entries, 0 to 505\n",
      "Data columns (total 14 columns):\n",
      "CRIM       506 non-null float64\n",
      "ZN         506 non-null int64\n",
      "INDUS      506 non-null float64\n",
      "CHAS       506 non-null int64\n",
      "NOX        506 non-null float64\n",
      "RM         506 non-null float64\n",
      "AGE        506 non-null float64\n",
      "DIS        506 non-null float64\n",
      "RAD        506 non-null int64\n",
      "TAX        506 non-null int64\n",
      "PTRATIO    506 non-null int64\n",
      "B          506 non-null float64\n",
      "LSTAT      506 non-null float64\n",
      "MEDV       506 non-null float64\n",
      "dtypes: float64(9), int64(5)\n",
      "memory usage: 55.5 KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(506, 14)\n"
     ]
    }
   ],
   "source": [
    "print(df.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Index(['CRIM', 'ZN', 'INDUS', 'CHAS', 'NOX', 'RM', 'AGE', 'DIS', 'RAD', 'TAX',\n",
      "       'PTRATIO', 'B', 'LSTAT', 'MEDV'],\n",
      "      dtype='object')\n"
     ]
    }
   ],
   "source": [
    "print(df.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>CRIM</th>\n",
       "      <th>ZN</th>\n",
       "      <th>INDUS</th>\n",
       "      <th>CHAS</th>\n",
       "      <th>NOX</th>\n",
       "      <th>RM</th>\n",
       "      <th>AGE</th>\n",
       "      <th>DIS</th>\n",
       "      <th>RAD</th>\n",
       "      <th>TAX</th>\n",
       "      <th>PTRATIO</th>\n",
       "      <th>B</th>\n",
       "      <th>LSTAT</th>\n",
       "      <th>MEDV</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>506.000000</td>\n",
       "      <td>506.000000</td>\n",
       "      <td>506.000000</td>\n",
       "      <td>506.000000</td>\n",
       "      <td>506.000000</td>\n",
       "      <td>506.000000</td>\n",
       "      <td>506.000000</td>\n",
       "      <td>506.000000</td>\n",
       "      <td>506.000000</td>\n",
       "      <td>506.000000</td>\n",
       "      <td>506.000000</td>\n",
       "      <td>506.000000</td>\n",
       "      <td>506.000000</td>\n",
       "      <td>506.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>3.613524</td>\n",
       "      <td>11.347826</td>\n",
       "      <td>11.136779</td>\n",
       "      <td>0.069170</td>\n",
       "      <td>0.554695</td>\n",
       "      <td>6.284634</td>\n",
       "      <td>68.574901</td>\n",
       "      <td>3.795043</td>\n",
       "      <td>9.549407</td>\n",
       "      <td>408.237154</td>\n",
       "      <td>18.083004</td>\n",
       "      <td>356.674032</td>\n",
       "      <td>12.653063</td>\n",
       "      <td>22.532806</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>8.601545</td>\n",
       "      <td>23.310593</td>\n",
       "      <td>6.860353</td>\n",
       "      <td>0.253994</td>\n",
       "      <td>0.115878</td>\n",
       "      <td>0.702617</td>\n",
       "      <td>28.148861</td>\n",
       "      <td>2.105710</td>\n",
       "      <td>8.707259</td>\n",
       "      <td>168.537116</td>\n",
       "      <td>2.280574</td>\n",
       "      <td>91.294864</td>\n",
       "      <td>7.141062</td>\n",
       "      <td>9.197104</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.006320</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.460000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.385000</td>\n",
       "      <td>3.561000</td>\n",
       "      <td>2.900000</td>\n",
       "      <td>1.129600</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>187.000000</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>0.320000</td>\n",
       "      <td>1.730000</td>\n",
       "      <td>5.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.082045</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>5.190000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.449000</td>\n",
       "      <td>5.885500</td>\n",
       "      <td>45.025000</td>\n",
       "      <td>2.100175</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>279.000000</td>\n",
       "      <td>17.000000</td>\n",
       "      <td>375.377500</td>\n",
       "      <td>6.950000</td>\n",
       "      <td>17.025000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.256510</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>9.690000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.538000</td>\n",
       "      <td>6.208500</td>\n",
       "      <td>77.500000</td>\n",
       "      <td>3.207450</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>330.000000</td>\n",
       "      <td>19.000000</td>\n",
       "      <td>391.440000</td>\n",
       "      <td>11.360000</td>\n",
       "      <td>21.200000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>3.677082</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>18.100000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.624000</td>\n",
       "      <td>6.623500</td>\n",
       "      <td>94.075000</td>\n",
       "      <td>5.188425</td>\n",
       "      <td>24.000000</td>\n",
       "      <td>666.000000</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>396.225000</td>\n",
       "      <td>16.955000</td>\n",
       "      <td>25.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>88.976200</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>27.740000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.871000</td>\n",
       "      <td>8.780000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>12.126500</td>\n",
       "      <td>24.000000</td>\n",
       "      <td>711.000000</td>\n",
       "      <td>22.000000</td>\n",
       "      <td>396.900000</td>\n",
       "      <td>37.970000</td>\n",
       "      <td>50.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             CRIM          ZN       INDUS        CHAS         NOX          RM  \\\n",
       "count  506.000000  506.000000  506.000000  506.000000  506.000000  506.000000   \n",
       "mean     3.613524   11.347826   11.136779    0.069170    0.554695    6.284634   \n",
       "std      8.601545   23.310593    6.860353    0.253994    0.115878    0.702617   \n",
       "min      0.006320    0.000000    0.460000    0.000000    0.385000    3.561000   \n",
       "25%      0.082045    0.000000    5.190000    0.000000    0.449000    5.885500   \n",
       "50%      0.256510    0.000000    9.690000    0.000000    0.538000    6.208500   \n",
       "75%      3.677082   12.000000   18.100000    0.000000    0.624000    6.623500   \n",
       "max     88.976200  100.000000   27.740000    1.000000    0.871000    8.780000   \n",
       "\n",
       "              AGE         DIS         RAD         TAX     PTRATIO           B  \\\n",
       "count  506.000000  506.000000  506.000000  506.000000  506.000000  506.000000   \n",
       "mean    68.574901    3.795043    9.549407  408.237154   18.083004  356.674032   \n",
       "std     28.148861    2.105710    8.707259  168.537116    2.280574   91.294864   \n",
       "min      2.900000    1.129600    1.000000  187.000000   12.000000    0.320000   \n",
       "25%     45.025000    2.100175    4.000000  279.000000   17.000000  375.377500   \n",
       "50%     77.500000    3.207450    5.000000  330.000000   19.000000  391.440000   \n",
       "75%     94.075000    5.188425   24.000000  666.000000   20.000000  396.225000   \n",
       "max    100.000000   12.126500   24.000000  711.000000   22.000000  396.900000   \n",
       "\n",
       "            LSTAT        MEDV  \n",
       "count  506.000000  506.000000  \n",
       "mean    12.653063   22.532806  \n",
       "std      7.141062    9.197104  \n",
       "min      1.730000    5.000000  \n",
       "25%      6.950000   17.025000  \n",
       "50%     11.360000   21.200000  \n",
       "75%     16.955000   25.000000  \n",
       "max     37.970000   50.000000  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#目标y （房屋价格）直方图/分布\n",
    "#1. 对连续型特征，可以用哪个函数可视化其分布？\n",
    "fig = plt.figure()\n",
    "sns.distplot(df[\"MEDV\"], bins=30, kde=True)\n",
    "plt.xlabel(\"Median vaue of owner-occupied homes\", fontsize=12)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<matplotlib.axes._subplots.AxesSubplot object at 0x00000280FEDBF580>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x00000280FEECD400>]],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "features = [\"MEDV\", \"CRIM\"]\n",
    "df[features].hist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x280fef65af0>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#2. 对两个连续型特征，可以用哪个函数得到这两个特征之间的相关性？\n",
    "cols = df.columns\n",
    "\n",
    "data_corr = df.corr()\n",
    "sns.heatmap(data_corr, annot=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 936x648 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "data_corr = data_corr.abs()\n",
    "plt.subplots(figsize=(13,9))\n",
    "sns.heatmap(data_corr, annot=True)\n",
    "\n",
    "sns.heatmap(data_corr, mask=data_corr < 0.5, cbar=False)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "#3. 如果发现特征之间有较强的相关性，在选择线性回归模型时应该采取什么措施。\n",
    "#在回归模型后加上正则项系数，常用的有L2, L1正则"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RAD and TAX = 0.91\n",
      "NOX and DIS = 0.77\n",
      "INDUS and NOX = 0.76\n",
      "AGE and DIS = 0.75\n",
      "LSTAT and MEDV = 0.74\n",
      "NOX and AGE = 0.73\n",
      "INDUS and TAX = 0.72\n",
      "INDUS and DIS = 0.71\n",
      "RM and MEDV = 0.70\n",
      "NOX and TAX = 0.67\n",
      "ZN and DIS = 0.66\n",
      "INDUS and AGE = 0.64\n",
      "CRIM and RAD = 0.63\n",
      "RM and LSTAT = 0.61\n",
      "NOX and RAD = 0.61\n",
      "INDUS and LSTAT = 0.60\n",
      "AGE and LSTAT = 0.60\n",
      "INDUS and RAD = 0.60\n",
      "NOX and LSTAT = 0.59\n",
      "CRIM and TAX = 0.58\n",
      "ZN and AGE = 0.57\n",
      "TAX and LSTAT = 0.54\n",
      "DIS and TAX = 0.53\n",
      "ZN and INDUS = 0.53\n",
      "ZN and NOX = 0.52\n",
      "AGE and TAX = 0.51\n",
      "PTRATIO and MEDV = 0.51\n"
     ]
    }
   ],
   "source": [
    "threshold = 0.5\n",
    "corr_list = []\n",
    "size = data_corr.shape[0]\n",
    "\n",
    "for i in range(0, size):\n",
    "    for j in range(i+1, size):\n",
    "        if (data_corr.iloc[i,j] >= threshold and data_corr.iloc[i,j] < 1) or (data_corr.iloc[i,j] < 0 and data_corr.iloc[i,j] >= threshold) :\n",
    "            corr_list.append([data_corr.iloc[i,j],i,j])\n",
    "            \n",
    "s_corr_list = sorted(corr_list, key=lambda x : -abs(x[0]))\n",
    "\n",
    "for v,i,j in s_corr_list:\n",
    "    print(\"%s and %s = %.2f\" % (cols[i], cols[j], v))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "#删除y大于等于50的样本\n",
    "#df = df[df.MEDV < 50]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "y = df['MEDV']\n",
    "X = df.drop('MEDV', axis = 1)\n",
    "\n",
    "#对房价做log变换\n",
    "log_y = np.log1p(y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0      1\n",
      "1      2\n",
      "2      2\n",
      "3      3\n",
      "4      3\n",
      "      ..\n",
      "501    1\n",
      "502    1\n",
      "503    1\n",
      "504    1\n",
      "505    1\n",
      "Name: RAD, Length: 506, dtype: int64\n",
      "     RAD_1  RAD_2  RAD_3  RAD_4  RAD_5  RAD_6  RAD_7  RAD_8  RAD_24\n",
      "0        1      0      0      0      0      0      0      0       0\n",
      "1        0      1      0      0      0      0      0      0       0\n",
      "2        0      1      0      0      0      0      0      0       0\n",
      "3        0      0      1      0      0      0      0      0       0\n",
      "4        0      0      1      0      0      0      0      0       0\n",
      "..     ...    ...    ...    ...    ...    ...    ...    ...     ...\n",
      "501      1      0      0      0      0      0      0      0       0\n",
      "502      1      0      0      0      0      0      0      0       0\n",
      "503      1      0      0      0      0      0      0      0       0\n",
      "504      1      0      0      0      0      0      0      0       0\n",
      "505      1      0      0      0      0      0      0      0       0\n",
      "\n",
      "[506 rows x 9 columns]\n"
     ]
    }
   ],
   "source": [
    "X[\"RAD\"].astype(\"object\")\n",
    "x_cat = X[\"RAD\"]\n",
    "print(x_cat)\n",
    "x_cat = pd.get_dummies(x_cat, prefix=\"RAD\")\n",
    "print(x_cat)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = X.drop(\"RAD\", axis=1)\n",
    "feat_names = X.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "#数值特征标准化\n",
    "#4. 当采用带正则的模型以及采用随机梯度下降优化算法时，需要对输入（连续型）特征进行去量纲预处理。课程代码给出了用标准化（StandardScaler）的结果，请改成最小最大缩放（MinMaxScaler）去量纲 ，并重新训练最小二乘线性回归、岭回归、和Lasso模型。\n",
    "from sklearn.preprocessing import StandardScaler, MinMaxScaler\n",
    "\n",
    "ss_X = StandardScaler()\n",
    "ss_y = StandardScaler()\n",
    "ss_log_y = StandardScaler()\n",
    "\n",
    "mm_X = MinMaxScaler()\n",
    "mm_y = MinMaxScaler()\n",
    "mm_log_y = MinMaxScaler()\n",
    "\n",
    "X_standard = ss_X.fit_transform(X)\n",
    "y_standard = ss_y.fit_transform(y.values.reshape(-1, 1))\n",
    "log_y_standard = ss_log_y.fit_transform(log_y.values.reshape(-1, 1))\n",
    "\n",
    "X_mm = mm_X.fit_transform(X)\n",
    "y_mm = mm_y.fit_transform(y.values.reshape(-1, 1))\n",
    "log_y_mm = mm_log_y.fit_transform(log_y.values.reshape(-1, 1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "fe_data = pd.DataFrame(data=X_mm, columns = feat_names, index = df.index)\n",
    "fe_data = pd.concat([fe_data, x_cat], axis = 1, ignore_index=False)\n",
    "\n",
    "fe_data[\"MEDV\"] = y_mm\n",
    "fe_data[\"log_MEDV\"] = log_y_mm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>CRIM</th>\n",
       "      <th>ZN</th>\n",
       "      <th>INDUS</th>\n",
       "      <th>CHAS</th>\n",
       "      <th>NOX</th>\n",
       "      <th>RM</th>\n",
       "      <th>AGE</th>\n",
       "      <th>DIS</th>\n",
       "      <th>TAX</th>\n",
       "      <th>PTRATIO</th>\n",
       "      <th>...</th>\n",
       "      <th>RAD_2</th>\n",
       "      <th>RAD_3</th>\n",
       "      <th>RAD_4</th>\n",
       "      <th>RAD_5</th>\n",
       "      <th>RAD_6</th>\n",
       "      <th>RAD_7</th>\n",
       "      <th>RAD_8</th>\n",
       "      <th>RAD_24</th>\n",
       "      <th>MEDV</th>\n",
       "      <th>log_MEDV</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.18</td>\n",
       "      <td>0.067815</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.314815</td>\n",
       "      <td>0.577505</td>\n",
       "      <td>0.641607</td>\n",
       "      <td>0.269203</td>\n",
       "      <td>0.208015</td>\n",
       "      <td>0.3</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.422222</td>\n",
       "      <td>0.666856</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.000236</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.242302</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.172840</td>\n",
       "      <td>0.547998</td>\n",
       "      <td>0.782698</td>\n",
       "      <td>0.348962</td>\n",
       "      <td>0.104962</td>\n",
       "      <td>0.5</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.368889</td>\n",
       "      <td>0.619696</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.000236</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.242302</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.172840</td>\n",
       "      <td>0.694386</td>\n",
       "      <td>0.599382</td>\n",
       "      <td>0.348962</td>\n",
       "      <td>0.104962</td>\n",
       "      <td>0.5</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.660000</td>\n",
       "      <td>0.833335</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.000293</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.063050</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.150206</td>\n",
       "      <td>0.658555</td>\n",
       "      <td>0.441813</td>\n",
       "      <td>0.448545</td>\n",
       "      <td>0.066794</td>\n",
       "      <td>0.6</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.631111</td>\n",
       "      <td>0.816001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.000705</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.063050</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.150206</td>\n",
       "      <td>0.687105</td>\n",
       "      <td>0.528321</td>\n",
       "      <td>0.448545</td>\n",
       "      <td>0.066794</td>\n",
       "      <td>0.6</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.693333</td>\n",
       "      <td>0.852567</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 23 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       CRIM    ZN     INDUS  CHAS       NOX        RM       AGE       DIS  \\\n",
       "0  0.000000  0.18  0.067815   0.0  0.314815  0.577505  0.641607  0.269203   \n",
       "1  0.000236  0.00  0.242302   0.0  0.172840  0.547998  0.782698  0.348962   \n",
       "2  0.000236  0.00  0.242302   0.0  0.172840  0.694386  0.599382  0.348962   \n",
       "3  0.000293  0.00  0.063050   0.0  0.150206  0.658555  0.441813  0.448545   \n",
       "4  0.000705  0.00  0.063050   0.0  0.150206  0.687105  0.528321  0.448545   \n",
       "\n",
       "        TAX  PTRATIO  ...  RAD_2  RAD_3  RAD_4  RAD_5  RAD_6  RAD_7  RAD_8  \\\n",
       "0  0.208015      0.3  ...      0      0      0      0      0      0      0   \n",
       "1  0.104962      0.5  ...      1      0      0      0      0      0      0   \n",
       "2  0.104962      0.5  ...      1      0      0      0      0      0      0   \n",
       "3  0.066794      0.6  ...      0      1      0      0      0      0      0   \n",
       "4  0.066794      0.6  ...      0      1      0      0      0      0      0   \n",
       "\n",
       "   RAD_24      MEDV  log_MEDV  \n",
       "0       0  0.422222  0.666856  \n",
       "1       0  0.368889  0.619696  \n",
       "2       0  0.660000  0.833335  \n",
       "3       0  0.631111  0.816001  \n",
       "4       0  0.693333  0.852567  \n",
       "\n",
       "[5 rows x 23 columns]"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fe_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(404, 21)"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import r2_score\n",
    "\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "#MinMaxScaler\n",
    "X_mm2 = fe_data.drop([\"MEDV\", \"log_MEDV\"], axis = 1)\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X_mm2, y_mm, random_state=33, test_size=0.2)\n",
    "X_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>columns</th>\n",
       "      <th>coef</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>RM</td>\n",
       "      <td>[0.4523767582987796]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ZN</td>\n",
       "      <td>[0.12923864998048062]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>RAD_24</td>\n",
       "      <td>[0.10322084272164077]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>B</td>\n",
       "      <td>[0.07889905355666509]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>CHAS</td>\n",
       "      <td>[0.05966007893211265]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>RAD_7</td>\n",
       "      <td>[0.03766434783111704]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>RAD_8</td>\n",
       "      <td>[0.030071178746459145]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>RAD_3</td>\n",
       "      <td>[0.02746524040913962]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>INDUS</td>\n",
       "      <td>[0.01381800352841409]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>AGE</td>\n",
       "      <td>[-0.0012284359596782786]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>RAD_4</td>\n",
       "      <td>[-0.008697752871012742]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>RAD_5</td>\n",
       "      <td>[-0.011516997287458544]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>RAD_2</td>\n",
       "      <td>[-0.0409703671242495]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>RAD_6</td>\n",
       "      <td>[-0.05573568575664989]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>RAD_1</td>\n",
       "      <td>[-0.08150080666898576]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>TAX</td>\n",
       "      <td>[-0.11354790724935232]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>NOX</td>\n",
       "      <td>[-0.15155316050144452]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>PTRATIO</td>\n",
       "      <td>[-0.18789839979885495]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>CRIM</td>\n",
       "      <td>[-0.22187425429881133]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>DIS</td>\n",
       "      <td>[-0.3862494055272409]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>LSTAT</td>\n",
       "      <td>[-0.4766745755939476]</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    columns                      coef\n",
       "5        RM      [0.4523767582987796]\n",
       "1        ZN     [0.12923864998048062]\n",
       "20   RAD_24     [0.10322084272164077]\n",
       "10        B     [0.07889905355666509]\n",
       "3      CHAS     [0.05966007893211265]\n",
       "18    RAD_7     [0.03766434783111704]\n",
       "19    RAD_8    [0.030071178746459145]\n",
       "14    RAD_3     [0.02746524040913962]\n",
       "2     INDUS     [0.01381800352841409]\n",
       "6       AGE  [-0.0012284359596782786]\n",
       "15    RAD_4   [-0.008697752871012742]\n",
       "16    RAD_5   [-0.011516997287458544]\n",
       "13    RAD_2     [-0.0409703671242495]\n",
       "17    RAD_6    [-0.05573568575664989]\n",
       "12    RAD_1    [-0.08150080666898576]\n",
       "8       TAX    [-0.11354790724935232]\n",
       "4       NOX    [-0.15155316050144452]\n",
       "9   PTRATIO    [-0.18789839979885495]\n",
       "0      CRIM    [-0.22187425429881133]\n",
       "7       DIS     [-0.3862494055272409]\n",
       "11    LSTAT     [-0.4766745755939476]"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.linear_model import LinearRegression\n",
    "lr = LinearRegression()\n",
    "lr.fit(X_train, y_train)\n",
    "\n",
    "y_test_pred_lr = lr.predict(X_test)\n",
    "y_trian_pred_lr = lr.predict(X_train)\n",
    "\n",
    "fs = pd.DataFrame({\"columns\":list(X_mm2.columns), \"coef\":list((lr.coef_.T))})\n",
    "fs.sort_values(by=['coef'], ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "r2 lr score of test:  0.6939789810509471\n",
      "r2 lr score of train:  0.7549146436868177\n"
     ]
    }
   ],
   "source": [
    "print('r2 lr score of test: ', r2_score(y_test, y_test_pred_lr))\n",
    "print('r2 lr score of train: ', r2_score(y_train, y_trian_pred_lr))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x280fef8cc70>"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 504x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "f, ax = plt.subplots(figsize=(7,5))\n",
    "f.tight_layout()\n",
    "ax.hist(y_train - y_trian_pred_lr, bins = 40, label=\"Residuals Linear\", color='b', alpha=0.5)\n",
    "ax.set_title(\"Histogram of Residuals\")\n",
    "ax.legend(loc='best')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAARgAAADQCAYAAADcQn7hAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy8li6FKAAAepklEQVR4nO3deXhU9dn/8fedkJAg0SAiyg4WFxAIGDZBH0REoKCItKVGfg8ghNKigl4VFa8HgVpF0ILoIxWppRZcKpvKLi1bIZpgwqIQlGIqiw+LEFkCIcz9+2MmNEKWSTJnzszkfl3XXJmZTM73I4Tbc77ne+4jqooxxjghyu0AxpjIZQXGGOMYKzDGGMdYgTHGOMYKjDHGMdXcDlAeV111lTZp0sTtGMaYi2zZsuWIqta5+P2wKjBNmjQhIyPD7RjGmIuISE5x79shkjHGMVZgjDGOsQJjjHGMawVGROJE5DMR2SoiX4jIRLeyGGOc4eYk71mgu6qeFJEYYKOILFfVNBczGWMCyLU9GPU66XsZ43vYlZfGuOzYsWM8/vjjnDp1qtLbcnUORkSiRSQLOASsVtVPi/lMqohkiEjG4cOHgx/SmCpk165dtG7dmldeeYUNGzZUenuuFhhVPa+qSUADoIOI3FzMZ95Q1WRVTa5T55J1PMaYAGrSpAkdO3Zk8+bN9OrVq9LbC4mzSKp6HFgLVP6/yBhTLrt27WLAgAH88MMPxMXF8cEHH5CcnByQbbt5FqmOiCT6nscDPYBdbuUxpqpRVWbNmkW7du1Yv3492dnZAR/DzT2Ya4F/iMg2IB3vHMzHLuYxpso4dOgQ9957L6NGjeK2225j+/bttG/fPuDjuHaaWlW3AW3dGt+Yqmz06NGsWrWK6dOn8/DDDxMV5cy+hoRTT97k5GS1ix2NqZi8vDxOnz5N7dq1+fbbbzl+/DitWrUKyLZFZIuqXjJxExKTvMYYZ2VlZZGcnMzgwYNRVRo2bBiw4lIaKzDGRDCPx8O0adPo2LEjx44dY8yYMYhI0MYPq34wxhj/fffddzz44IOsWbOG++67jzfeeIOrrroqqBmswBgToWJjY9m3bx+zZ8/moYceCuqeSyE7RDImgpw4cYLJkydz7tw5rrzySnbs2MHw4cNdKS5gBcaYiJGWlkbbtm2ZMGECa9euBaBaNXcPUqzAGBPmCgoKmDRpEl27dqWgoIB169Zx1113uR0LsDkYY8Jeamoqb731FikpKbz22mtcccUVbke6wAqMMWFIVSkoKCAmJoZHHnmEHj168MADD7gd6xJWYIwJM8eOHWPUqFHUrFmTN998k6SkJJKSktyOVSybgzEmjKxdu5Y2bdqwYMECmjVrRqhf6mMFxpgwkJ+fz5NPPkn37t2Ji4tj8+bNPP30066dfvaXFRhjwsB3333H66+/zvDhw8nMzAxYQyin2RyMMSFKVVm6dCk//elPadSoETt37qRevXpuxyoXNzvaNRSRf4jITt99kR51K4sxoaawIVS/fv1YsmQJQNgVF3B3D6YAeFxVPxeRBGCLiKxW1S9dzGSM65YvX86QIUPIzc1l+vTp3HPPPW5HqjA374t0UFU/9z0/AewE6ruVx5hQMHHiRPr06cPVV19Neno6jz76qGPd5oIhJJKLSBO87TMvuS+SMVVJ586defTRR0lPTw9KQyinud4yU0RqAuuA51R1YTHfTwVSARo1anRLTk5OkBMa4xyPx8P06dPJy8tj/PjxbsepsJBsmem7J/UCYF5xxQXsxmsmcu3fv5+7776bxx9/nMzMTDwej9uRAs7Ns0gCzAF2qurLbuUwxg0LFiygdevWbNq0idmzZ/O3v/0trOdaSuLmf1EXYDDQXUSyfI8+LuYxJihycnIYNGgQzZo1IzMz09WGUE5z875IG4HI/FM1phjffPMNTZo0oXHjxqxZs4bOnTsTExPjdixHRd4+mTEhpqCggMmTJ9O8eXOWLl0KwO233x7xxQXsUgFjHLV3714efPBBNm3aREpKCl27dnU7UlBZgTHGIe+++y6pqamICPPmzQvJhlBOswJjjEPy8vJISkriL3/5C02aNHE7jitcX2hXHnZvahPq1q1bx8GDBxk0aBCqisfjITo62u1YjgvJhXbGRIr8/Hyeeuop7rjjDl588UU8Hg8iUiWKS2mswBhTSbt27aJz58688MILDB8+nPXr10fkormKsDkYYyrhwIED3HLLLcTHx7No0SL69+/vdqSQYgXGmArIz88nNjaWevXq8fLLL9OvX7+wbAjltDL340SkrojMEZHlvtctROQh56MZE5qWL1/Oddddx2effQbAyJEjrbiUwJ8DxT8DK4HCP8HdwBinAhkTqvLy8nj44Yfp06cPiYmJ1KhRw+1IIc+fAnOVqr4PeABUtQA472gqY0LM1q1bSU5O5tVXX2XMmDGkp6dz8803ux0r5PkzB3NKRGoDCiAinYBcR1MZE2I++ugjvv/+e1auXEnPnj3djhM2ylxoJyLtgJnAzcAOoA4wUFW3OR/vx2yhnQmmAwcOkJOTQ+fOnSkoKCA3N5fatWu7HSsklbTQrsw9GF/X//8CbsDbXiFbVc85kNGYkLFw4UJGjBhBYmIi2dnZVKtWzYpLBfhzFuk3QE1V/UJVdwA1ReTXzkczJvhOnjzJQw89xP3330+zZs1Yvnw51arZao6K8meSd4SqHi98oarHgBGBGFxE/iQih0RkRyC2Z0xlHDp0iLZt2/LWW2/x9NNPs2nTJq6//nq3Y4U1fwpMlBTp5yci0UBsgMb/M9ArQNsyplLq1KlDz549Wbt2Lc8991yVaAjlNH8KzErgfRG5U0S6A+8AKwIxuKquB74PxLaMqYi9e/fSu3dv9uzZg4jw2muvcfvtt7sdK2L4U2DGAX8HRgG/AdYATzgZqigRSRWRDBHJOHz4cLCGNRFOVXn77bdp06YNmzZt4quvvnI7UkRyvR+M766OH6tqmauW7DS1CYTjx4/Tb9AQNq5cQvUGLbiq7+PEXFGXlE6N+F3/8L+bohvKfZpaRN5X1Z+LyHZ8i+yKUtXWAc5oTFAMGzuejas+IvG2wVzeaSASFY0Cf037N4AVmQAq7fzbo76vfYMRxBinLM7cz4RFWRw9fIhqV1yNp1Z3rhncnOrXNr/ks39N+7cVmAAqscCo6kHfGaM5qtrDicFF5B2gG3CViOwDJqjqHCfGMpFtceZ+nv3wC47n/WcNqODd9T53dB9HPp6GJ/8M9YbNJCo2rtjiYgKv1BVEqnpeRE6LyBWqGvDrj1T1l4Hepql6FmfuZ8x7WZe871Hl5NYVHFvzJhJTndq9RiPRduo5mPxZongG2C4iq4FThW+q6iOOpTKmFMXtrVzMc/YURz5+mbyvPyWuSVtq9xlDtQRb6h9s/hSYpb6HMa5bnLmfx97L8vYOKYVUq47n7ClqdR9BQnI/RKxHrhv8udhxrojEAjfiPaTNVtV8x5MZc5FnFm+/cKanOJ5zZ8nd/D6XJ99DdI0rqPvL31thcVmZBUZE+gB/BPbgnTdrKiIjVXW50+GMAe9ey9j3si5dK1FE/qG9HPloKueO/JuYWtdSs1UPKy4hwJ9DpJeBO1T1awARuQ7vIZMVGOO4lNmb+eeekq8mUfVwIn0Jx9bPJTougat/NpH4ZrdUeLzEeJsEDiR/CsyhwuLi8y/gkEN5TBW3OHM/U1dmc+B4HgJlzrXk/vMdcv/5DvHNO1G718NE17iiUuP3bXNtpX7e/Jg/BeYLEVkGvI93DuZnQLqIDABQ1YUO5jNVyOLM/Ty1cDt557wtn0s7JNKCfKRaLAlt+xCdUIeare+iyEX/FbZwyz5baBdA/hSYOOD/gP/yvT4MXAn0w/s7YAXGBMTUldkXiktJPPl5HFszm3Pf76PuL58n+rJaJLQJXI/c0+fK2mcy5eHPWaShwQhiqrbFmfvZfzyv1M+cPZDNkY+nUXDsOy7vNBD8vFC3y3VX8rPkRhcOveolxpc5lgkM6wVoXFfW6Wf1nOeHtA84vnEe0Qm1qfvA88Q1LPni+/iYKHZO7n3J+/3b1r/wvOlTS4utTwE4yjJFWIExrlmcuZ/f/i2Lso5KtCCfk9s/ocaNt1G75yii4mqW+NmYKOH5AWVf6J/SsVGxRS2lY6Myf9b4zwqMcUVJ1w8VUlXydm8mrtktRMXGc83/e4no+MtL3WZifAzP3tPyR3sqJSmcyH3n0285r0q0CL/s2NAmeAOsxIZTIvJYaT+oqi87kqgU1nAqcrT8nxWcyi9+Qtdz5iRHV/0vp3eup1b34Vzevr9f25z+iyS/iosJvIrcFynB9/UGoD3woe91P2B9YOOZquSZxdtLLC5n/r2dI0tf5vyJoyTeNpiEW/r5vd2pK7OtwISY0vrBTAQQkVVAO1U94Xv9LPC3oKQzEWdx5n7mlTCheyJrOd+v/F+q1bqGax6cSvV6N5Rr2wfszFDI8WcOphFQ9OLGfKCJI2lMxJv40RclLqCLa9iKmm17U6vbUKJi48u97XqJ5f8Z4yx/rgZ7G/hMRJ4VkQnAp8BfAjG4iPQSkWwR+VpEngzENk3oWpy5n2On/9PDRVU5kbWCI0uno6rE1G5A7Z6/rlBxiY+J5rd3l2+PxzjPn4V2z4nIcuA231tDVTWzsgP72nG+BtwF7MN7+cGHqvplZbdtQtOzH35x4fn507kcXTGTvK/SiGuchBacRWLiKrTd+onx/PbuG2z+JQT5e5q6BvCDqr4lInVEpKmq7q3k2B2Ar1X1XwAi8i5wL2AFJkIVdqDL+9cWji6bzvkzJ6jVfTgJyfdUqLVCTLQwdWAbKywhzJ9+MBOAZLxnk94CYoC/Al0qOXZ94Nsir/cBHYsZPxVIBWjUyBZBhTtPfh5HPn6J6MtqcfXPJxF7dVO/fi5KoHq1KPJ8q/Jq1YhhQj//1rwY9/izB3Mf0Bb4HEBVD4hIQuk/4pfiFmUXd/+lN4A3wLsOJgDjGhfs3r2bxLgojhNP3V/8jmpX1icqprrfPx/tW6FrBSW8+LNfmq/e1XgKICKXBWjsfUDDIq8bAAcCtG0TIjweD3/4wx9o1aoVt5z8jJhoIbZus3IVF4Bz55WpK7MdSmmc4k+BeV9E/ggkisgI4BPgzQCMnQ40F5Gmvp6/g/jPYj4TAQ4cOECvXr147LHH6NWrFy+M+zVTB7ahfimnk6OjSr7a0Na5hB9/ziJNE5G7gB/wzsP8j6quruzAqlogIqOBlUA08CdV/aKMHzNhYsWKFaSkpJCXl8esWbNITU1FROhfx/v9oo2lwHu8nNKpEcmNr+Tx97dyvphLWGydS/jxZ5J3iqqOA1YX816lqOoyYFllt2NCT0JCAj/5yU+YO3cuN95444++V1xjKQX+sevwhYsNLy5Ats4lPPlziHRXMe9d2mzDVHnp6em89NJLAHTp0oW0tLRLiguUfKhT+H7/tvV5fkAr6ifGI3jXuTw/oJVN8IahEvdgRGQU8GvgOhHZVuRbCcAmp4OZ8HH+/HleeOEFnn32WerXr09qaioJCQkl9sgtqaNc0UOg/m3rW0GJAKXtwczHe+X0Et/XwsctqpoShGwmDHzzzTd069aNZ555hoEDB5KVlUVCQumrGH579w3Ex0T/6D07BIpMpV1NnQvkisgM4PsiV1MniEhHVf00WCFNaMrLy6NTp07k5eXx9ttvk5KS4ldn/8I9k6I9cm2pf2QqseHUhQ+IZOJt11C4DiYKyFDVdkHI9yPWcCo0nD59mho1agCwaNEikpKSaNrUvxW5JjKV1HDKn0le0SJVSFU9WKvNKmv9+vXcdNNNzJs3D4D77rvPiospkT8F5l8i8oiIxPgej+K9u6OpQvLz83n66afp1q0bMTExNG/e3O1IJgz4U2B+BdwK7Oc/FySmOhnKhJbs7GxuvfVWnn/+eYYNG0ZWVhYdOnRwO5YJA/6s5D2Edxm/qaK2b9/O3r17WbBgAQMGDHA7jgkjpa2DeUJVXxSRmRR/lfMjjiYzrjpy5AibN2+mX79+DBw4kB49epCYmOh2LBNmStuD2en7aqdtqpiVK1cyZMgQTp8+TU5ODomJiVZcTIWUtg7mI9/XucGLY9x05swZxo0bxyuvvELLli1ZsWKFFRZTKaUdIn1EMYdGhVT1HkcSGVecPXuWjh07sm3bNh5++GGmTJlCfLxdvWwqp7RDpGm+rwOAa/C2yQT4JfCNg5mMC6pXr05KSgpTpkyhV69ebscxEcKflbzrVfX2st4LBlvJG1gHDhzgoYceYty4cXTr1s3tOCaMVWYlbx0RaVZkQ02BOoEMZ4Jv0aJFtG7dmnXr1nHggHUqNc7wp8CMBdaKyFoRWQv8AxhTmUFF5Gci8oWIeETkkqpnnHPy5ElGjBjBgAEDaNy4MZ9//jkPPPCA27FMhPJnod0KEWkOFHYO2qWqZys57g68czt/rOR2TDm9++67zJkzhyeffJKJEycSGxvrdiQTwfxpmVkDeAxorKojRKS5iNygqh9XdFBV3enbdkU3Ycrh/PnzZGdn06JFC4YNG0a7du1o1y7oF8ObKsifQ6S38N7wvrPv9T7gd44luoiIpIpIhohkHD58OFjDRoycnBzuuOMOunbtypEjR4iKirLiYoLGnwJznaq+CJwDUNU8ir9p2o+IyCcisqOYx73lCaiqb6hqsqom16ljc8vlMW/ePFq3bk1WVhYzZsygdu3abkcyVYw/fV3yRSSe/9x47TqgzDkYVe1RyWymgs6dO8eQIUOYP38+Xbp04e2337aeLcYV/uzBTABWAA1FZB6wBnjC0VSmUmJiYoiNjWXy5MmsXbvWiotxTakL7cQ7C9sAOA10wntolKaqRyo1qMh9wEy862mOA1mqendZP2cL7Up27tw5Jk2axKBBg2jZsiWqapPoJmhKWmhX6iGSqqqILFbVW4ClgQqjqouARYHaXlW3e/duUlJSyMjIIC4ujpYtW1pxMSHBn0OkNBFp73gSU26qyuzZs2nbti179uzhgw8+YPz48W7HMuYCfyZ57wB+JSLfAKfwHiapqrZ2Mpgp25///GdSU1O58847mTt3LvXr220/TGjxp8DYbWJDzIkTJ0hISOCBBx5AVRkyZAhRUf7sjBoTXCX+VopInIiMAX4L9AL2q2pO4SNoCc0FZ86cYcyYMbRp04bc3FyqV6/OsGHDrLiYkFXab+ZcIBnYjncv5qWgJDLF2r59O+3bt2fGjBn07dvXriEyYaG0Q6QWqtoKQETmAJ8FJ5IpyuPxMHPmTMaNG0diYiLLli2jd287ajXhobQ9mHOFT1S1IAhZTAk+/PBDevbsybZt26y4mLBS2h5MGxH5wfdcgHjf68KzSJc7nq4KW7JkCe3bt6devXosXryYmjVr2toWE3ZK3INR1WhVvdz3SFDVakWeW3FxSGFDqP79+zNlyhQAEhISrLiYsGQ3sQ8h6enppKSk8PXXX19oCGVMOLMCEyKWLFnCwIEDufbaa/n73/9uTbhNRLAFFC4rvNj09ttvZ+TIkWzdutWKi4kYVmBcNH/+fO68807y8/OpVasWr776KrVq1XI7ljEBYwXGBbm5uaSkpJCSksLZs2c5duyY25GMcYQVmCDbsGEDbdq04b333mPSpEmsW7eOunXruh3LGEe4MskrIlOBfnibie8BhqrqcTeyBJPH42H06NFUq1aNjRs30qlTJ7cjGeMot/ZgVgM3+1o+7AaecilHUHz11VecOHGCqKgoFi1aRGZmphUXUyW4UmBUdVWRyw/S8LbljDiFDaGSkpJ46ilvDW3WrBkJCQkuJzMmOEJhDmYYsNztEIF25MgRBgwYQGpqKp07d75QYIypShybgxGRT4BrivnWeFVd4vvMeKAAmFfKdlKBVIBGjRo5kDTwNm/ezP3338/Ro0eZNm0aY8eOtZ4tpkpyrMCUdV8kEflvoC9wp5ZyawNVfQN4A7x3FQhoSIfUq1ePpk2bsmzZMpKSktyOY4xrXPnfqoj0AsYB96jqaTcyBNr27dsZO3Ysqkrjxo3ZuHGjFRdT5bm13/4qkACsFpEsEZnlUo5K83g8zJgxg/bt2zN//nxycrzdRO3qZ2NcWgejqj9xY9xAO3jwIEOGDGHVqlX07duXOXPmcPXVV7sdy5iQYVdTV5Cq0rt3b3bv3s3rr7/OyJEjba/FmItYgSmnU6dOERsbS0xMDK+99hq1a9fmxhtvdDuWMSHJzp2WQ0ZGBu3atbvQCKpLly5WXIwphRUYP5w/f57f//73dO7cmdOnT9OjR6ln4I0xPnaIVIacnBwGDx7Mhg0b+PnPf86sWbOsZ4sxfrICU4ajR4/y5ZdfMnfuXAYPHmwTucaUgxWYYuTm5rJw4UKGDh1Ku3btyMnJ4bLLLnM7ljFhx+ZgLlLYEGrEiBHs3r0bwIqLMRVkBcbn3LlzPPPMM3Tr1o3o6Gg2btzI9ddf73YsY8KaHSLhXTTXp08fPvnkE4YOHcqMGTOsZ4sxAVClC0zhRdwiwvDhwxk5ciQDBw50OZUxkaPKHiIdPXqU+++/n9mzZwPwi1/8woqLMQFWJQvM6tWradWqFUuXLiU/P9/tOMZErCpVYM6cOcPYsWPp2bMniYmJfPrpp4wePdrtWMZErCpVYNLS0pgxYwajR49my5Yt1hDKGIdF/CSvx+MhIyODDh060K1bN3bs2EGLFi3cjmVMleBWy8zJIrLN181ulYjUc2KcgwcP0rt3b2699Va+/PJLACsuxgSRW4dIU1W1taomAR8D/xPoAZYsWUKrVq3YsGEDM2fO5Kabbgr0EMaYMrh147Ufiry8DAjo3QJGjx5N//79adSoEVu2bGHUqFF2kaIxLnBtkldEnhORb4EUStmDEZFUEckQkYzDhw/7te0GDRrwxBNPkJaWZnsuxrhISrklUeU27MeN13yfewqIU9UJZW0zOTlZMzIyApjSGBMIIrJFVZMvft+1G68VMR9YCpRZYIwx4cWts0jNi7y8B9jlRg5jjLPcWgfzgojcAHiAHOBXLuUwxjjIrRuv3e/GuMaY4KpSlwoYY4LLCowxxjGOnaZ2gogcxjtnU5argCMOx/GXZSmeZSleuGZprKp1Ln4zrAqMv0Qko7hz8m6wLMWzLMWLtCx2iGSMcYwVGGOMYyK1wLzhdoAiLEvxLEvxIipLRM7BGGNCQ6TuwRhjQoAVGGOMYyK2wASrLaefWaaKyC5fnkUikuhilp+JyBci4hERV06HikgvEckWka9F5Ek3Mvhy/ElEDonIDrcyFMnSUET+ISI7fX8/j7qYJU5EPhORrb4sEyu8MVWNyAdweZHnjwCzXMzSE6jmez4FmOJilpuAG4C1QLIL40cDe4BmQCywFWjh0p/F7UA7YIdbfx9FslwLtPM9TwB2u/jnIkBN3/MY4FOgU0W2FbF7MOpwW85yZlmlqgW+l2lAAxez7FTVbLfGBzoAX6vqv1Q1H3gXuNeNIKq6HvjejbEvpqoHVfVz3/MTwE6gvktZVFVP+l7G+B4V+vcTsQUG/G/LGWTDgOVuh3BRfeDbIq/34dI/pFAlIk2Atnj3HNzKEC0iWcAhYLWqVihLWBcYEflERHYU87gXQFXHq2pDYB7g6C0cy8ri+8x4oMCXx9UsLiqu+7qtlfARkZrAAmDMRXvhQaWq59V7148GQAcRubki2wnrG69pCLXlLCuLiPw30Be4U30Ht25lcdk+oGGR1w2AAy5lCSkiEoO3uMxT1YVu5wFQ1eMishboBZR7Mjys92BKE0ptOUWkFzAOuEdVT7uVI0SkA81FpKmIxAKDgA9dzuQ68d5XZw6wU1VfdjlLncIznSISD/Sggv9+InYlr4gswHu25EJbTlXd71KWr4HqwFHfW2mq6kqbUBG5D5gJ1AGOA1mqeneQM/QBpuM9o/QnVX0umOMXyfEO0A1vW4L/Ayao6hyXsnQFNgDb8f7OAjytqstcyNIamIv37ycKeF9VJ1VoW5FaYIwx7ovYQyRjjPuswBhjHGMFxhjjGCswxhjHWIExxjgmrBfaGWeISG1gje/lNcB54LDvdQffNUTBzrQSGOi7TseECTtNbUolIs8CJ1V12kXvC97fH0+xPxi48YMyjnGGHSIZv4nIT3zXNM0CPgcaisjxIt8fJCJv+p7XFZGFIpLh6y3SqZjtDff1x1np6w/zTAnjXCsi+4qsLh3q662zVUTe8nc8E3x2iGTKqwUwVFV/JSKl/f68Aryoqmm+q4M/Boq7YK6D7/18IF1EPgZOFh0HwLsjAyLSBu9lF7eq6vcicmU5xzNBZAXGlNceVU3343M9gBsKCwNQS0TiVTXvos+tVNVjACKyGOgKrChlnO7Ae6r6PUDh13KMZ4LICowpr1NFnnv4cfuFuCLPBf8mhC+eBCx8feriDxbZbnETh/6OZ4LI5mBMhfkmXo+JSHMRiQLuK/LtT4DfFL4QkaQSNtNTRBJFpAbeznb/LGPYT4BBhYdGRQ6R/B3PBJEVGFNZ4/Ae0qzB2+ul0G+ALr7J2C+BESX8/Ea8/XoygXdUNau0wVR1G/AisN7XcW1qOcczQWSnqY1rRGQ4cLOqjnE7i3GG7cEYYxxjezDGGMfYHowxxjFWYIwxjrECY4xxjBUYY4xjrMAYYxzz/wF/M5G40BZVigAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 288x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(4,3))\n",
    "plt.scatter(y_train, y_trian_pred_lr)\n",
    "plt.plot([-3,3], [-3,3], '--k')\n",
    "plt.axis('tight')\n",
    "plt.xlabel('True price')\n",
    "plt.ylabel('Predicted price')\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "r2 score of ridge of test: 0.6961684813070043\n",
      "r2 score of ridge of train: 0.7548524440445555\n"
     ]
    }
   ],
   "source": [
    "#L2正则-岭回归\n",
    "\n",
    "from sklearn.linear_model import RidgeCV\n",
    "\n",
    "alphas = [0.01, 0.1, 1, 10, 100]\n",
    "\n",
    "ridge = RidgeCV(alphas = alphas, store_cv_values = True)\n",
    "\n",
    "ridge.fit(X_train, y_train)\n",
    "\n",
    "y_test_pred_ridge = ridge.predict(X_test)\n",
    "y_train_pred_ridge = ridge.predict(X_train)\n",
    "\n",
    "print('r2 score of ridge of test:', r2_score(y_test, y_test_pred_ridge))\n",
    "print('r2 score of ridge of train:', r2_score(y_train, y_train_pred_ridge))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "alpha is: 0.1\n"
     ]
    }
   ],
   "source": [
    "mse_mean = np.mean(ridge.cv_values_, axis=0)\n",
    "plt.plot(np.log10(alphas), mse_mean.reshape(len(alphas), 1))\n",
    "\n",
    "plt.xlabel('log(alpha)')\n",
    "plt.ylabel('mse')\n",
    "plt.show()\n",
    "\n",
    "print('alpha is:', ridge.alpha_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "r2 score of lasso of test: 0.6953719445869475\n",
      "r2 score of lasso of train: 0.7546505272258799\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "d:\\develop\\python3\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:1088: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n"
     ]
    }
   ],
   "source": [
    "from sklearn.linear_model import LassoCV\n",
    "\n",
    "lasso = LassoCV()\n",
    "\n",
    "lasso.fit(X_train, y_train)\n",
    "y_test_pred_lasso = lasso.predict(X_test)\n",
    "y_train_pred_lasso = lasso.predict(X_train)\n",
    "\n",
    "print('r2 score of lasso of test:', r2_score(y_test, y_test_pred_lasso))\n",
    "print('r2 score of lasso of train:', r2_score(y_train, y_train_pred_lasso))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "alpha is: 9.422274692522746e-05\n"
     ]
    }
   ],
   "source": [
    "mses = np.mean(lasso.mse_path_, axis=1)\n",
    "plt.plot(np.log10(lasso.alphas_), mses)\n",
    "\n",
    "plt.xlabel('log(alpha)')\n",
    "plt.ylabel('mse')\n",
    "plt.show()\n",
    "\n",
    "print('alpha is:', lasso.alpha_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>columes</th>\n",
       "      <th>coef_lr</th>\n",
       "      <th>coef_ridge</th>\n",
       "      <th>coef_lasso</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>RM</td>\n",
       "      <td>[0.4523767582987796]</td>\n",
       "      <td>[0.4491038546449264]</td>\n",
       "      <td>0.452181</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ZN</td>\n",
       "      <td>[0.12923864998048062]</td>\n",
       "      <td>[0.12596554053117892]</td>\n",
       "      <td>0.120726</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>RAD_24</td>\n",
       "      <td>[0.10322084272164077]</td>\n",
       "      <td>[0.09991109055252334]</td>\n",
       "      <td>0.099135</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>B</td>\n",
       "      <td>[0.07889905355666509]</td>\n",
       "      <td>[0.07932699767467777]</td>\n",
       "      <td>0.077388</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>CHAS</td>\n",
       "      <td>[0.05966007893211265]</td>\n",
       "      <td>[0.06007499595486543]</td>\n",
       "      <td>0.060328</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>RAD_7</td>\n",
       "      <td>[0.03766434783111704]</td>\n",
       "      <td>[0.03671245510862997]</td>\n",
       "      <td>0.041814</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>RAD_8</td>\n",
       "      <td>[0.030071178746459145]</td>\n",
       "      <td>[0.030422813178308417]</td>\n",
       "      <td>0.035717</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>RAD_3</td>\n",
       "      <td>[0.02746524040913962]</td>\n",
       "      <td>[0.028356025506823812]</td>\n",
       "      <td>0.036489</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>INDUS</td>\n",
       "      <td>[0.01381800352841409]</td>\n",
       "      <td>[0.011490792371264491]</td>\n",
       "      <td>0.000287</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>AGE</td>\n",
       "      <td>[-0.0012284359596782786]</td>\n",
       "      <td>[-0.0010767577526078487]</td>\n",
       "      <td>-0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>RAD_4</td>\n",
       "      <td>[-0.008697752871012742]</td>\n",
       "      <td>[-0.00864560855301022]</td>\n",
       "      <td>-0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>RAD_5</td>\n",
       "      <td>[-0.011516997287458544]</td>\n",
       "      <td>[-0.01118296833785859]</td>\n",
       "      <td>-0.002728</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>RAD_2</td>\n",
       "      <td>[-0.0409703671242495]</td>\n",
       "      <td>[-0.03904179174908595]</td>\n",
       "      <td>-0.026297</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>RAD_6</td>\n",
       "      <td>[-0.05573568575664989]</td>\n",
       "      <td>[-0.055449259201464346]</td>\n",
       "      <td>-0.046790</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>RAD_1</td>\n",
       "      <td>[-0.08150080666898576]</td>\n",
       "      <td>[-0.08108275650485464]</td>\n",
       "      <td>-0.070050</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>TAX</td>\n",
       "      <td>[-0.11354790724935232]</td>\n",
       "      <td>[-0.11019202334135825]</td>\n",
       "      <td>-0.097387</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>NOX</td>\n",
       "      <td>[-0.15155316050144452]</td>\n",
       "      <td>[-0.14590977845888475]</td>\n",
       "      <td>-0.138506</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>PTRATIO</td>\n",
       "      <td>[-0.18789839979885495]</td>\n",
       "      <td>[-0.18736905307582474]</td>\n",
       "      <td>-0.183970</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>CRIM</td>\n",
       "      <td>[-0.22187425429881133]</td>\n",
       "      <td>[-0.21261949557826726]</td>\n",
       "      <td>-0.203124</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>DIS</td>\n",
       "      <td>[-0.3862494055272409]</td>\n",
       "      <td>[-0.3748666096217981]</td>\n",
       "      <td>-0.369251</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>LSTAT</td>\n",
       "      <td>[-0.4766745755939476]</td>\n",
       "      <td>[-0.4746026914756749]</td>\n",
       "      <td>-0.477812</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    columes                   coef_lr                coef_ridge  coef_lasso\n",
       "5        RM      [0.4523767582987796]      [0.4491038546449264]    0.452181\n",
       "1        ZN     [0.12923864998048062]     [0.12596554053117892]    0.120726\n",
       "20   RAD_24     [0.10322084272164077]     [0.09991109055252334]    0.099135\n",
       "10        B     [0.07889905355666509]     [0.07932699767467777]    0.077388\n",
       "3      CHAS     [0.05966007893211265]     [0.06007499595486543]    0.060328\n",
       "18    RAD_7     [0.03766434783111704]     [0.03671245510862997]    0.041814\n",
       "19    RAD_8    [0.030071178746459145]    [0.030422813178308417]    0.035717\n",
       "14    RAD_3     [0.02746524040913962]    [0.028356025506823812]    0.036489\n",
       "2     INDUS     [0.01381800352841409]    [0.011490792371264491]    0.000287\n",
       "6       AGE  [-0.0012284359596782786]  [-0.0010767577526078487]   -0.000000\n",
       "15    RAD_4   [-0.008697752871012742]    [-0.00864560855301022]   -0.000000\n",
       "16    RAD_5   [-0.011516997287458544]    [-0.01118296833785859]   -0.002728\n",
       "13    RAD_2     [-0.0409703671242495]    [-0.03904179174908595]   -0.026297\n",
       "17    RAD_6    [-0.05573568575664989]   [-0.055449259201464346]   -0.046790\n",
       "12    RAD_1    [-0.08150080666898576]    [-0.08108275650485464]   -0.070050\n",
       "8       TAX    [-0.11354790724935232]    [-0.11019202334135825]   -0.097387\n",
       "4       NOX    [-0.15155316050144452]    [-0.14590977845888475]   -0.138506\n",
       "9   PTRATIO    [-0.18789839979885495]    [-0.18736905307582474]   -0.183970\n",
       "0      CRIM    [-0.22187425429881133]    [-0.21261949557826726]   -0.203124\n",
       "7       DIS     [-0.3862494055272409]     [-0.3748666096217981]   -0.369251\n",
       "11    LSTAT     [-0.4766745755939476]     [-0.4746026914756749]   -0.477812"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fs = pd.DataFrame({\"columes\":list(X_mm2.columns), \"coef_lr\":list((lr.coef_.T)), \"coef_ridge\": list((ridge.coef_.T)), \"coef_lasso\":list((lasso.coef_.T))})\n",
    "fs.sort_values(by=['coef_lr'], ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 5. 代码中给出了岭回归（RidgeCV）和Lasso（LassoCV）超参数（alpha_）调优的过程，请结合两个最佳模型以及最小二乘线性回归模型的结果，给出什么场合应该用岭回归，什么场合用Lasso，什么场合用最小二乘。\n",
    "\n",
    "#岭回归使线性回归系数收缩，模型稳定，当输入特征之间存在共线性时使用岭回归\n",
    "\n",
    "#Lasso也会收缩回归系数，当正则参数取合适值时，Lasso使得有些线性回归系数为0，得到稀疏模型。\n",
    "#当输入特征多，有些特征与目标变量之间相关性很弱时，Lasso 可能只选择强相关的特征，模型解释性号"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
